细胞-组织认知下的主动配电系统双层多目标控制策略

近年来,大规模的可再生能源已被广泛集成到电力系统中,配电系统的主导形式也从传统配电网逐渐转变到微网集群。由于风、光等可再生能源具有不确定性,为了优化可再生资源在电力系统中的整合效果,并考虑对微网集群的智能化管理以改善配网运行水平,基于"细胞-组织"结构提出了一种新型双层多目标动态能量管理策略;然后在主动配电系统中将主动配电网和微网集群之间的关系进行了分类,并提出能量博弈矩阵和双层多目标控制策略对主动配电网和微网集群的能量管理进行描述和实现;最后采用分层-带精英策略的快速非支配排序混合遗传算法(hierarchical genetic algorithm-NSGA-Ⅱ,HGA-NSGA-Ⅱ)求解能量管理问题,并通过对基于多微网的混合IEEE-33电力系统的仿真分析,验证了所提控制策略在功耗水平、能源利用率和仿真运算时间等方面上的优势。

Bi-Level Multi-Objective Control Strategy of Active Distribution System Under the Cognition of Cell-Tissue Theory

In recent years,large-scale renewable energy has been widely integrated into the power system,and the dominant form of distribution system has also gradually changed from traditional distribution network to microgrid cluster. To optimize the integration of renewable resource with uncertainty into the power system, considering the intelligent management of microgrid cluster to improve the distribution network performance,this paper proposes a new bi-level multiobjective dynamic energy management strategy based on the cell-tissue theory. The relationship between active distribution network and microgrid cluster are classified,and described by the energy game matrix and bi-level multi-objective control strategy. Finally,this paper adopts the hierarchical genetic algorithm-NSGA-Ⅱ to solve the energy management problem,and verifies the advantages of the proposed control strategy in power consumption,energy efficiency and simulation operation time through the analysis on the IEEE 33-node network based on microgrid cluster.